基于Pytorch框架的深度学习MODNet网络精细人像分割系统源码
第一步：准备数据
人像精细分割数据，可分割出头发丝，为PPM-100开源数据
第二步：搭建模型
MODNet网络结构如图所示，主要包含3个部分：semantic estimation（S分支）、detail prediction（D分支）、semantic-detail fusion（F分支）。
网络结构简单描述一下：
输入一幅图像I，送入三个模块：S、D、F；
S模块：在低分辨率分支进行语义估计，在backbone最后一层输出接上e-ASPP得到语义feature map Sp；
D模块：在高分辨率分支进行细节预测，通过融合来自低分辨率分支的信息得到细节feature map Dp；
F模块：融合来自低分辨率分支和高分辨率分支的信息，得到alpha matte ap；
对S、D、F模块，均使用来自GT的显式监督信息进行监督训练。
第三步：代码
1）损失函数为：L2损失
2）网络代码：
import torch
import torch.nn as nn
import torch.nn.functional as F
from .backbones import SUPPORTED_BACKBONES
#------------------------------------------------------------------------------
# MODNet Basic Modules
#------------------------------------------------------------------------------
class IBNorm(nn.Module):
""" Combine Instance Norm and Batch Norm into One Layer
"""
def __init__(self, in_channels):
super(IBNorm, self).__init__()
in_channels = in_channels
self.bnorm_channels = int(in_channels / 2)
self.inorm_channels = in_channels - self.bnorm_channels
self.bnorm = nn.BatchNorm2d(self.bnorm_channels, affine=True)
self.inorm = nn.InstanceNorm2d(self.inorm_channels, affine=False)
def forward(self, x):
bn_x = self.bnorm(x[:, :self.bnorm_channels, ...].contiguous())
in_x = self.inorm(x[:, self.bnorm_channels:, ...].contiguous())
return torch.cat((bn_x, in_x), 1)
class Conv2dIBNormRelu(nn.Module):
""" Convolution + IBNorm + ReLu
"""
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, groups=1, bias=True,
with_ibn=True, with_relu=True):
super(Conv2dIBNormRelu, self).__init__()
layers = [
nn.Conv2d(in_channels, out_channels, kernel_size,
stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
]
if with_ibn:
layers.append(IBNorm(out_channels))
if with_relu:
layers.append(nn.ReLU(inplace=True))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class SEBlock(nn.Module):
""" SE Block Proposed in https://arxiv.org/pdf/1709.01507.pdf
"""
def __init__(self, in_channels, out_channels, reduction=1):
super(SEBlock, self).__init__()
self.pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(in_channels, int(in_channels // reduction), bias=False),
nn.ReLU(inplace=True),
nn.Linear(int(in_channels // reduction), out_channels, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
w = self.pool(x).view(b, c)
w = self.fc(w).view(b, c, 1, 1)
return x * w.expand_as(x)
#------------------------------------------------------------------------------
# MODNet Branches
#------------------------------------------------------------------------------
class LRBranch(nn.Module):
""" Low Resolution Branch of MODNet
"""
def __init__(self, backbone):
super(LRBranch, self).__init__()
enc_channels = backbone.enc_channels
self.backbone = backbone
self.se_block = SEBlock(enc_channels[4], enc_channels[4], reduction=4)
self.conv_lr16x = Conv2dIBNormRelu(enc_channels[4], enc_channels[3], 5, stride=1, padding=2)
self.conv_lr8x = Conv2dIBNormRelu(enc_channels[3], enc_channels[2], 5, stride=1, padding=2)
self.conv_lr = Conv2dIBNormRelu(enc_channels[2], 1, kernel_size=3, stride=2, padding=1, with_ibn=False, with_relu=False)
def forward(self, img, inference):
enc_features = self.backbone.forward(img)
enc2x, enc4x, enc32x = enc_features[0], enc_features[1], enc_features[4]
enc32x = self.se_block(enc32x)
lr16x = F.interpolate(enc32x, scale_factor=2, mode='bilinear', align_corners=False)
lr16x = self.conv_lr16x(lr16x)
lr8x = F.interpolate(lr16x, scale_factor=2, mode='bilinear', align_corners=False)
lr8x = self.conv_lr8x(lr8x)
pred_semantic = None
if not inference:
lr = self.conv_lr(lr8x)
pred_semantic = torch.sigmoid(lr)
return pred_semantic, lr8x, [enc2x, enc4x]
class HRBranch(nn.Module):
""" High Resolution Branch of MODNet
"""
def __init__(self, hr_channels, enc_channels):
super(HRBranch, self).__init__()
self.tohr_enc2x = Conv2dIBNormRelu(enc_channels[0], hr_channels, 1, stride=1, padding=0)
self.conv_enc2x = Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=2, padding=1)
self.tohr_enc4x = Conv2dIBNormRelu(enc_channels[1], hr_channels, 1, stride=1, padding=0)
self.conv_enc4x = Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1)
self.conv_hr4x = nn.Sequential(
Conv2dIBNormRelu(3 * hr_channels + 3, 2 * hr_channels, 3, stride=1, padding=1),
Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1),
Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1),
)
self.conv_hr2x = nn.Sequential(
Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1),
Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1),
Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1),
Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1),
)
self.conv_hr = nn.Sequential(
Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=1, padding=1),
Conv2dIBNormRelu(hr_channels, 1, kernel_size=1, stride=1, padding=0, with_ibn=False, with_relu=False),
)
def forward(self, img, enc2x, enc4x, lr8x, inference):
img2x = F.interpolate(img, scale_factor=1/2, mode='bilinear', align_corners=False)
img4x = F.interpolate(img, scale_factor=1/4, mode='bilinear', align_corners=False)
enc2x = self.tohr_enc2x(enc2x)
hr4x = self.conv_enc2x(torch.cat((img2x, enc2x), dim=1))
enc4x = self.tohr_enc4x(enc4x)
hr4x = self.conv_enc4x(torch.cat((hr4x, enc4x), dim=1))
lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False)
hr4x = self.conv_hr4x(torch.cat((hr4x, lr4x, img4x), dim=1))
hr2x = F.interpolate(hr4x, scale_factor=2, mode='bilinear', align_corners=False)
hr2x = self.conv_hr2x(torch.cat((hr2x, enc2x), dim=1))
pred_detail = None
if not inference:
hr = F.interpolate(hr2x, scale_factor=2, mode='bilinear', align_corners=False)
hr = self.conv_hr(torch.cat((hr, img), dim=1))
pred_detail = torch.sigmoid(hr)
return pred_detail, hr2x
class FusionBranch(nn.Module):
""" Fusion Branch of MODNet
"""
def __init__(self, hr_channels, enc_channels):
super(FusionBranch, self).__init__()
self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2)
self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1)
self.conv_f = nn.Sequential(
Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1),
Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False),
)
def forward(self, img, lr8x, hr2x):
lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False)
lr4x = self.conv_lr4x(lr4x)
lr2x = F.interpolate(lr4x, scale_factor=2, mode='bilinear', align_corners=False)
f2x = self.conv_f2x(torch.cat((lr2x, hr2x), dim=1))
f = F.interpolate(f2x, scale_factor=2, mode='bilinear', align_corners=False)
f = self.conv_f(torch.cat((f, img), dim=1))
pred_matte = torch.sigmoid(f)
return pred_matte
#------------------------------------------------------------------------------
# MODNet
#------------------------------------------------------------------------------
class MODNet(nn.Module):
""" Architecture of MODNet
"""
def __init__(self, in_channels=3, hr_channels=32, backbone_arch='mobilenetv2', backbone_pretrained=True):
super(MODNet, self).__init__()
self.in_channels = in_channels
self.hr_channels = hr_channels
self.backbone_arch = backbone_arch
self.backbone_pretrained = backbone_pretrained
self.backbone = SUPPORTED_BACKBONES[self.backbone_arch](self.in_channels)
self.lr_branch = LRBranch(self.backbone)
self.hr_branch = HRBranch(self.hr_channels, self.backbone.enc_channels)
self.f_branch = FusionBranch(self.hr_channels, self.backbone.enc_channels)
for m in self.modules():
if isinstance(m, nn.Conv2d):
self._init_conv(m)
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.InstanceNorm2d):
self._init_norm(m)
if self.backbone_pretrained:
self.backbone.load_pretrained_ckpt()
def forward(self, img, inference):
pred_semantic, lr8x, [enc2x, enc4x] = self.lr_branch(img, inference)
pred_detail, hr2x = self.hr_branch(img, enc2x, enc4x, lr8x, inference)
pred_matte = self.f_branch(img, lr8x, hr2x)
return pred_semantic, pred_detail, pred_matte
def freeze_norm(self):
norm_types = [nn.BatchNorm2d, nn.InstanceNorm2d]
for m in self.modules():
for n in norm_types:
if isinstance(m, n):
m.eval()
continue
def _init_conv(self, conv):
nn.init.kaiming_uniform_(
conv.weight, a=0, mode='fan_in', nonlinearity='relu')
if conv.bias is not None:
nn.init.constant_(conv.bias, 0)
def _init_norm(self, norm):
if norm.weight is not None:
nn.init.constant_(norm.weight, 1)
nn.init.constant_(norm.bias, 0)
第四步：搭建GUI界面
第五步：整个工程的内容
有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码
代码见：基于Pytorch框架的深度学习MODNet网络精细人像分割系统源码
有问题可以私信或者留言，有问必答
原文地址：https://blog.csdn.net/m0_59023219/article/details/142416589
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